Spectral imaging is a branch of spectroscopy and of photography in which at least some spectral information is collected from an image plane (e.g., two-dimensional image) for a scene of interest. An image capture device may be aimed at a scene in order to capture image information for that scene. A variety of spectral imaging methodologies are known. Examples include hyperspectral imaging, multispectral imaging (a type of hyperspectral imaging), full spectral imaging, imaging spectroscopy, chemical imaging, and the like. Historically, hyperspectral imaging and multi-spectral image analysis have been associated with satellite, airborne, or large scale operations using large, expensive camera systems that are not well-suited for handheld operation or routine business and consumer applications.
Spectral imaging generally involves capturing spectral information from one or more portions of the electromagnetic spectrum. Although spectral information for any wavelengths in the electromagnetic spectrum may be used, often spectroscopy uses spectral information for wavelengths in the range from about 100 nm to about 14,000 nm. For reference, it is often convenient to divide the span of the electromagnetic spectrum into the following bands: ultraviolet (UV) band from 100 nm to 400 nm; visible (VIS) band from 400 to 700 nm; near infrared (NIR) band from 700 to 1500 nm; short-wave infrared (SWIR) band from 1500 to 3000 nm; mid-wave infrared (MWIR) band from 3000 to 5000 nm; and long-wave infrared (LWIR) band from 5000 to 14000 nm. The ultraviolet band includes the following sub-bands: far ultraviolet (FUV) band from 122 to 200 nm; middle ultraviolet (MUV) band from 200 to 300 nm; and near ultraviolet (NUV) band from 300 to 400 nm. The ultraviolet band also is divided into the following sub-bands: ultraviolet C (UVC) band from 100 to 280 nm; ultraviolet B (UVB) band from 280 to 315 nm; and ultraviolet A (UVA) band from 315 to 400 nm.
Spectral information captured for a scene may be represented as an image cube, which is a type of data cube. A data cube generally is a three dimensional array of data values. In spectroscopy, one kind of data cube results when a spectrally-resolved image is represented as a three dimensional volume in which the captured image is represented in a two dimensional image while spectral information associated with individual pixels, or groups of pixels, is incorporated into at least a third dimension of the data cube.
Conventional hyperspectral imaging is a powerful but expensive analysis technology for remotely determining the chemical composition of a surface. For example, hyperspectral imaging typically generates a data cube with two spatial dimensions and one spectral dimension. The two spatial dimensions correspond to the spatial data that might be represented in a common digital photograph. However, each spatial pixel in a hyperspectral data cube also is associated with an electromagnetic spectral array spanning wavelengths that often extend well beyond the visible spectrum to higher and/or lower wavelengths.
A conventional hyperspectral imaging system scans a scene to capture image information. Line scanning often is used. For example, an illustrative, conventional hyperspectral system has a linear array of 256 imaging elements. This linear array is scanned across the target surface in a line-scan or “push-broom” format. In this manner the linear array can generate a three-dimensional data cube with two spatial dimensions and one spectral dimension. A typical data cube may have dimensions of 256×256 spatial dimensions by 320 spectral bins. Spatial lines or “frames” are acquired sequentially at a frame rate of 100 to 400 frames per second. This line scan modality, acquires complete image information over a second or longer. This makes it difficult to acquire high resolution images for moving subjects.
Although conventional hyperspectral imaging provides powerful analysis capabilities, additional significant limitations exist for any widespread application of this technology as conventionally practiced. Foremost is a high price of $100,000 to $200,000 per system. The linear detection array imposes a scanning modality requiring a precision fixture with a uniform scanning speed between target surface and linear detection array. The line scan requirement also eliminates the possibility of acquiring a truly simultaneous image since the detector scans across the target surface in sequential lines. Undue movement of the target with regard to the scanning coordinates tends to result in an unrecognizable spatial shape in the resulting image. With movement, the resulting spectral information for a given pixel also may be distorted. The scanning requirement, alignment complexity and necessary fixturing often results in a stationary system that is not easily transported. With a high price tag and precise scanning restrictions, conventional hyperspectral imaging systems are limited primarily to inspection systems in large scale operations for high-volume production lines such as food processing, garbage sorting, or mineral analysis. Conventional hyperspectral imaging is not well suited for mobile applications, high-resolution systems, multi-line operation, bench-scale laboratory analysis, small business or consumer uses.
Multispectral imaging also offers the potential for remotely determining the chemical composition of a surface. Similar to a hyperspectral image, a multispectral image contains data with both spatial and spectral dimensions. Unlike the hyperspectral image, which contains a full spectrum for each spatial pixel, a multispectral image contains a fixed number of broad spectral bands for each spatial pixel. Conventional multispectral imaging systems typically are designed for one or more specific initial uses but are difficult to reconfigure for other uses.
The digital photography and digital camera industries have developed high quality, low cost sensors that capture images for portions of the electromagnetic spectrum that fall within the visible spectrum, namely, 400 nm to 700 nm. Both professional and consumer markets show strong interest in and benefit from these technologies. Digital cameras and phones typically use either charge-coupled device (CCD) or CMOS image sensors to capture images. These CCD and CMOS sensors can be obtained with a wide range of resolutions, e.g., from 540×720 (0.39 MP), to 5184×3456 (18 MP) in a number of digital, single lens reflex cameras (Canon EOS 60D Digital SLR Camera with lens kit, 5184×3456 pixels, 18 MP, $1300) and lower cost, compact cameras (Canon PowerShot, A1300, 16 MP, $119). Recent advances have led to miniaturization such that these cameras are routinely integrated into small form factors as thin as 7.6 mm (0.3 in.), including the lens. An example of a product that integrates such a miniaturized form factor are the iPhone 5 smartphone available from Apple Inc. as well as other smartphones. In addition to smartphone technology, there are a number of other mobile and economical computing technologies such as tablet, mini-tablet, laptop, and desktop technologies. Each of these alternative technologies shares many capabilities with those of smartphones.
Smartphone and other mobile computing technologies such as touch sensitive tablets have significant computing and connection power as well as the inclusion of digital cameras. While early models had only limited photography capability, the latest smartphones, such as the iPhone 5 and Samsung Galaxy S III have dual digital cameras (front and back facing) with resolutions of 8 MP/1.2 MP and 8 MP/1.9 MP, respectively. In addition to camera features, these smartphones have the capability for GPS location and navigation, as well as sensing vertical and horizontal phone orientation. Communication capabilities include Bluetooth and Wi-Fi standards.
Researchers at the University of Illinois are developing a smartphone-based spectrometer. See Liz Ahlberg, Cradle Turns Smartphone into Handheld Biosensor, University of Illinois, News Bureau, Public Affairs, May 23, 2013. A custom cradle holds the smartphone in fixed alignment with optical components that include a photonic crystal biosensor. This device detects shifts in the resonant wavelength of the biosensor on the order of 5 nm. The target must be dissolved in a small vial of liquid and placed on a microscope slide. The slide is in turn placed in a slot on the cradle attached to the smartphone. While this device contains a smartphone display for observing a single, resonant wavelength of a biosensor for the given target substance on the microscope slide, it does not provide mobile, high-resolution, chemical imaging capability. This device cannot be used for high volume or instantaneous analysis of a target substance due to the time required to dissolve the target substance in water, place the solution on a microscope slide and await a 30 second analysis which consults a web-based, data base.
Heinold, U.S. Pat. Application No. 20140022381, describes a spectral imaging apparatus that includes a multi-camera system having multiple camera elements, a set of filter elements attached to the camera elements and a light-sensor array, and a second set of filter elements attached to the light-sensor elements.
Even with the multitude of technologies described above, there presently exists no mobile, economical and convenient method or apparatus to rapidly, remotely and accurately detect, locate or quantify information of interest, such as the presence or amount of a target substance at particular location(s) in a scene. Therefore, there is a strong need for a spectral imaging system that has detection capabilities associated with hyperspectral and multispectral imaging systems but is economical, able to acquire high-resolution data rapidly from moving targets, is mobile, and is suitable for agricultural, medical, veterinary, sanitation, industrial, business, and consumer uses. Also, there is a strong need for spectral imaging systems whose detection capabilities can be easily changed on demand to be able to detect information for a wide range of desired applications.